Preliminary Notes

Comparing Anon vs. Registered Volunteers vs. Turkers

This section has a series of boxplots that compare performance of anonymous volunteers, registered volunteers, a single turker, and 5 turkers with majority vote. Here, a the single turker is the first turker to complete that set of routes.

For each granularity and label type, an ANOVA test was run, with the p-value being reported in the top-right corner of each boxplot. I appended a ** if the p-value was less than 0.01, and just a * if the p-value was less than 0.05, to make finding significant results easier.

To make room for the p-value on each boxplot, I expanded the y-limit past 1.0, and then added a dotted line at the 1.0 mark so that we still have that reference point. However, it makes the graphs look a bit ugly, so LMK if you want to remove it.

Takeaways:

Raw Accuracy

Defined as \(\frac{TP + TN}{TP + TN + FP + FN}\). Just the percentage of things they got correct.

Recall

Defined as \(\frac{TP}{TP + FN}\). High recall means that they found most of the issues/features.

Precision

Defined as \(\frac{TP}{TP + FP}\). High precision means that they rarely placed a label when they shouldn’t have.

F-measure

Defined as \(2 * \frac{precision * recall}{precision + recall}\). It is essentially a balance between recall and precision.

Specificity

Defined as \(\frac{TN}{TN + FP}\). Similar to precision, high specificity means that they rarely placed a label when they shouldn’t have, but specificity gives more weight to true negatives, while precision gives more weight to true positives.

Volunteer Data

In this section, there are a series of histograms that help to visualize the distribution of volunteers’ accuracy. For each accuracy measure, there is a grid of histograms split by label type and granularity (street, 5 meter, 10 meter).

Note that these histograms have lines representing the mean of each group (not the median; lmk if you want to see median instead).

Raw accuracy

Defined as \(\frac{TP + TN}{TP + TN + FP + FN}\). Just the percentage of things they got correct.

Recall

Defined as \(\frac{TP}{TP + FN}\). High recall means that they found most of the issues/features.

Precision

Defined as \(\frac{TP}{TP + FP}\). High precision means that they rarely placed a label when they shouldn’t have.

Note: Very little confidence should be given to precision for the NoSidewalk label, since GT labelers only placed the label at intersections and at places where a sidewalk ends.

F-measure

Defined as \(2 * \frac{precision * recall}{precision + recall}\). It is essentially a balance between recall and precision.

Specificity

Defined as \(\frac{TN}{TN + FP}\). Similar to precision, high specificity means that they rarely placed a label when they shouldn’t have, but specificity gives more weight to true negatives, while precision gives more weight to true positives.

Turker Data

Comparing effects of number of turkers and vote type on accuracy

In this section, there are a series of line graphs to help visualize how the number of turkers used and the method of voting affects the various accuracy measures. For each accuracy measure, there is a grid of line graphs, split by label type and granularity (same as above). However, each graph also has a line for each of the voting methods. You will notice that all voting methods are equivalent when looking at only one turker.

Raw Accuracy

Defined as \(\frac{TP + TN}{TP + TN + FP + FN}\). Just the percentage of things they got correct.

Recall

Defined as \(\frac{TP}{TP + FN}\). High recall means that they found most of the issues/features.

Precision

Defined as \(\frac{TP}{TP + FP}\). High precision means that they rarely placed a label when they shouldn’t have.

F-measure

Defined as \(2 * \frac{precision * recall}{precision + recall}\). It is essentially a balance between recall and precision.

Specificity

Defined as \(\frac{TN}{TN + FP}\). Similar to precision, high specificity means that they rarely placed a label when they shouldn’t have, but specificity gives more weight to true negatives, while precision gives more weight to true positives.

Removing Low Severity

In this section, we are looking at how accuracy is effected when we remove low severity labels from the GT set. The idea is that higher severity problems (as defined by the GT labelers) will be easier for crowd workers to find, resulting in higher recall. This would be a nice result to have, to say that crowd workers can at least find the most severe problems. The two expected outcomes here: recall goes up, and for precision to go down. I’ve included all accuracy types for now in case we see anything interesting.

Note: Low severity labels were removed from the ground truth data only. This is why we expect precision to go down, as legitimate problems have been removed from the ground truth, that crowd workers may have seen. Removing only crowd worker labels, but not GT labels, would likely result in higher precision and lower recall. Removing labels from both GT and crowd workers would probably result in lower precision and recall, due to the variability in how people label severity. Discrepancy between GT and crowd worker severity ratings will be addressed in a separate section.

How many low severity problems are there?

Below is a graph that shows the number of conditions (sets of routes) containing at least one label (split by label type), and how that is affected by removing low severity labels. On that first graph, the horizontal line shows the number of conditions that is in the current data set (some still need to be added once I fix some bugs and such).

The second graph shows the GT label counts, and how they are affected by removing low severity labels.

Raw Accuracy

Defined as \(\frac{TP + TN}{TP + TN + FP + FN}\). Just the percentage of things they got correct.

Recall

Defined as \(\frac{TP}{TP + FN}\). High recall means that they found most of the issues/features.

Precision

Defined as \(\frac{TP}{TP + FP}\). High precision means that they rarely placed a label when they shouldn’t have.

F-measure

Defined as \(2 * \frac{precision * recall}{precision + recall}\). It is essentially a balance between recall and precision.

Specificity

Defined as \(\frac{TN}{TN + FP}\). Similar to precision, high specificity means that they rarely placed a label when they shouldn’t have, but specificity gives more weight to true negatives, while precision gives more weight to true positives.

Incorporating single-user clustering

Label counts

Expected: There will be significantly fewer ramp labels, and slightly fewer labels from the other label types: since ramp labels often come in pairs, although a much lower distance threshold is being used for the clustering of ramp labels, we can expect a fair number of legitimate ramp labels to be excluded.

Observed: It is interesting that a higher proportion of NoSidewalk labels were removed than any other label type. I just used the 7.5 meter distance threshold that we used for Obstacle and SurfaceProblem for the NoSidewalk. This certainly indicates that there are quite a few users (of all user groups) who placed the NoSidewalk label frequently (possibly in the same way that Jon places NoSidewalk labels).

For turkers, after single-user clustering the percentage of ramp labels removed is actually much lower than that of other the label types, and the percentage of NoCurbRamp labels removed is the lowest for each user group.

What I find most interesting about comparing user groups is the difference in this graph between registered users and turkers (I talk specifically about them, since the group of anon users is quite small – though it is interesting that it anonymous users seem to match up with turkers). I don’t really have a guess right now for why 22% of registered users CurbRamp labels are gone, but only 7% of turker CurbRamps are.

SKIP THE REST OF THIS SECTION

Expected: For higher vote thresholds, reduction in labels might be more pronounced,

Expected: For higher vote thresholds, and for larger numbers of turkers being clustered, the reduction in labels might be more pronounced. The rationale is that in both those situations, there are more turkers involved, and thus there is a higher probability that one of the labels in the cluster is removed after single-user clustering… Increasing the probability that said cluster will no longer make the vote threshold.

Observed: In the histograms below, we see that both of the above turned out to be correct. More clusters no longer met the vote threshold for larger numbers of turkers, and for higher vote thresholds.

Recall

Expected: Recall will be slightly worse for all non-ramp label types: there are some cases where there are, in fact, multiple distinct issues in close proximity to one another. In such cases, clustering will remove one of the labels from the volunteer’s data, thus lowering recall.

Recall with be significantly worse for CurbRamp and NoCurbRamp labels: due to there being significantly fewer ramp labels.

Observed: At least for individual users, recall does decrease very slightly, and more so for ramp label types. But this is a very small difference; average recall decreased by about 0.01 for ramp label types, and by 0.005 for non ramp label types… This will likely be more pronounced once we look at clustered turkers.

Precision

Expected: Precision will improve slightly for all label types: in situations where a user labeled the same problem/feature multiple times, clustering should remove the duplicate label, thus improving precision.

Observed: In almost all cases, we do see a slight improvement in precision. The outliers are CurbRamp labels, which have a sizable improvement in precision (especially for registered users); anonymous users, whose precision does not seem to have been affected; and SurfaceProblem labels for turker, where precision actually slightly decreased.

Specificity

Expected: Specificity will improve slightly across the board: fewer labels => more true negatives and fewer false positives => higher specificity.

Observed: It is not exceptionally obvious from the graph, but specificity does improve slightly across the board. There is no way that specificity could get worse, since the formula includes only true negatives and false positives, which can only improve if we take away some labels. The largest improvement is with CurbRamps placed by registered users.

Variance in accuracy (NOT READY)

Expected: Variance will be slightly larger for precision and f-measure, for turkers with vote thresholds greater than 1: fewer labels => fewer labels that pass majority vote => fewer true/false positives => more sets of turkers with no true or false positives (for a given label type) => more sets of turkers with null precision (since denominator is TP + FP), and f-measure (since precision is used to compute f-measure) => larger confidence intervals for those accuracy types (b/c smaller n).

Observed: TBD